3. Setting up the system

3.1. Setting global variables in Python

The global variables in Python are controlled via the espressomd.system.System class. Global system variables can be read and set in Python simply by accessing the attribute of the corresponding Python object. Those variables that are already available in the Python interface are listed in the following. Note that for the vectorial properties box_l and periodicity, component-wise manipulation like system.box_l[0] = 1 or in-place operators like += or *= are not allowed and result in an error. This behavior is inherited, so the same applies to a after a = system.box_l. If you want to use a vectorial property for further calculations, you should explicitly make a copy e.g. via a = numpy.copy(system.box_l).

  • box_l

    (float[3]) Simulation box lengths of the cuboid box used by ESPResSo. Note that if you change the box length during the simulation, the folded particle coordinates will remain the same, i.e., the particle stay in the same image box, but at the same relative position in their image box. If you want to scale the positions, use the command change_volume_and_rescale_particles().

  • periodicity

    (int[3]) Specifies periodicity for the three directions. ESPResSo can be instructed to treat some dimensions as non-periodic. By default ESPResSo assumes periodicity in all directions which equals setting this variable to [True, True, True]. A dimension is specified as non-periodic via setting the periodicity variable for this dimension to False. E.g. Periodicity only in z-direction is obtained by [False, False, True]. Caveat: Be aware of the fact that making a dimension non-periodic does not hinder particles from leaving the box in this direction. In this case for keeping particles in the simulation box a constraint has to be set.

  • time_step

    (float) Time step for MD integration.

  • time

    (float) The simulation time.

  • min_global_cut

    (float) Minimal total cutoff for real space. Effectively, this plus the skin is the minimally possible cell size. ESPResSo typically determines this value automatically, but some algorithms, virtual sites, require you to specify it manually.

  • max_cut_bonded

    read-only Maximal cutoff of bonded real space interactions.

  • max_cut_nonbonded

    read-only Maximal cutoff of bonded real space interactions.

3.1.1. Accessing module states

Some variables like or are no longer directly available as attributes. In these cases they can be easily derived from the corresponding Python objects like:

n_part = len(system.part[:].pos)

or by calling the corresponding get_state() methods like:

temperature = system.thermostat.get_state()[0]['kT']

gamma = system.thermostat.get_state()[0]['gamma']

gamma_rot = system.thermostat.get_state()[0]['gamma_rotation']

3.2. Cellsystems

This section deals with the flexible particle data organization of ESPResSo. Due to different needs of different algorithms, ESPResSo is able to change the organization of the particles in the computer memory, according to the needs of the used algorithms. For details on the internal organization, refer to section Internal particle organization.

3.2.1. Global properties

The properties of the cell system can be accessed by espressomd.system.System.cell_system:

(int[3]) 3D node grid for real space domain decomposition (optional, if unset an optimal set is chosen automatically). The domain decomposition can be visualized with samples/visualization_cellsystem.py.

(float) Skin for the Verlet list. This value has to be set, otherwise the simulation will not start.

Details about the cell system can be obtained by espressomd.system.System.cell_system.get_state():

  • cell_grid Dimension of the inner cell grid.

  • cell_size Box-length of a cell.

  • local_box_l Local simulation box length of the nodes.

  • max_cut Maximal cutoff of real space interactions.

  • n_nodes Number of nodes.

  • type The current type of the cell system.

  • verlet_reuse Average number of integration steps the Verlet list is re-used.

3.2.2. Domain decomposition

Invoking set_domain_decomposition() selects the domain decomposition cell scheme, using Verlet lists for the calculation of the interactions. If you specify use_verlet_lists=False, only the domain decomposition is used, but not the Verlet lists.

system = espressomd.System(box_l=[1, 1, 1])


The domain decomposition cellsystem is the default system and suits most applications with short ranged interactions. The particles are divided up spatially into small compartments, the cells, such that the cell size is larger than the maximal interaction range. In this case interactions only occur between particles in adjacent cells. Since the interaction range should be much smaller than the total system size, leaving out all interactions between non-adjacent cells can mean a tremendous speed-up. Moreover, since for constant interaction range, the number of particles in a cell depends only on the density. The number of interactions is therefore of the order \(N\) instead of order \(N^2\) if one has to calculate all pair interactions.

3.2.3. N-squared

Invoking set_n_square() selects the very primitive N-squared cellsystem, which calculates the interactions for all particle pairs. Therefore it loops over all particles, giving an unfavorable computation time scaling of \(N^2\). However, algorithms like MMM1D or the plain Coulomb interaction in the cell model require the calculation of all pair interactions.

system = espressomd.System(box_l=[1, 1, 1])


In a multiple processor environment, the N-squared cellsystem uses a simple particle balancing scheme to have a nearly equal number of particles per CPU, \(n\) nodes have \(m\) particles, and \(p-n\) nodes have \(m+1\) particles, such that \(n \cdot m + (p - n) \cdot (m + 1) = N\), the total number of particles. Therefore the computational load should be balanced fairly equal among the nodes, with one exception: This code always uses one CPU for the interaction between two different nodes. For an odd number of nodes, this is fine, because the total number of interactions to calculate is a multiple of the number of nodes, but for an even number of nodes, for each of the \(p-1\) communication rounds, one processor is idle.

E.g. for 2 processors, there are 3 interactions: 0-0, 1-1, 0-1. Naturally, 0-0 and 1-1 are treated by processor 0 and 1, respectively. But the 0-1 interaction is treated by node 1 alone, so the workload for this node is twice as high. For 3 processors, the interactions are 0-0, 1-1, 2-2, 0-1, 1-2, 0-2. Of these interactions, node 0 treats 0-0 and 0-2, node 1 treats 1-1 and 0-1, and node 2 treats 2-2 and 1-2.

Therefore it is highly recommended that you use N-squared only with an odd number of nodes, if with multiple processors at all.

3.3. CUDA

CudaInitHandle() command can be used to choose the GPU for all subsequent GPU-computations. Note that due to driver limitations, the GPU cannot be changed anymore after the first GPU-using command has been issued, for example lbfluid. If you do not choose the GPU manually before that, CUDA internally chooses one, which is normally the most powerful GPU available, but load-independent.

system = espressomd.System(box_l=[1, 1, 1])

dev = system.cuda_init_handle.device
system.cuda_init_handle.device = dev

The first invocation in the sample above returns the id of the set graphics card, the second one sets the device id.

3.3.1. GPU Acceleration with CUDA


Feature CUDA required

ESPResSo is capable of GPU acceleration to speed up simulations. Not every simulation method is parallelizable or profits from GPU acceleration. Refer to Available simulation methods to check whether your desired method can be used on the GPU. In order to use GPU acceleration you need a NVIDIA GPU and it needs to have at least compute capability 2.0.

For more information please check espressomd.cuda_init.CudaInitHandle.

3.3.2. List available CUDA devices

If you want to list available CUDA devices, you should call espressomd.cuda_init.CudaInitHandle.list_devices():

>>> import espressomd
>>> system = espressomd.System(box_l=[1, 1, 1])
>>> print(system.cuda_init_handle.list_devices())
{0: 'GeForce RTX 2080', 1: 'GeForce GT 730'}

This method returns a dictionary containing the device id as key and the device name as its value.

To get more details on the CUDA devices for each MPI node, call espressomd.cuda_init.CudaInitHandle.list_devices_properties():

>>> import pprint
>>> import espressomd
>>> system = espressomd.System(box_l=[1, 1, 1])
>>> pprint.pprint(system.cuda_init_handle.list_devices_properties())
{'seraue': {0: {'name': 'GeForce RTX 2080',
                'compute_capability': (7, 5),
                'cores': 46,
                'total_memory': 8370061312},
            1: {'name': 'GeForce GT 730',
                'compute_capability': (3, 5),
                'cores': 2,
                'total_memory': 1014104064}}}

3.3.3. Selection of CUDA device

When you start pypresso your first GPU should be selected. If you wanted to use the second GPU, this can be done by setting espressomd.cuda_init.CudaInitHandle.device as follows:

>>> import espressomd
>>> system = espressomd.System(box_l=[1, 1, 1])
>>> system.cuda_init_handle.device = 1

Setting a device id outside the valid range or a device which does not meet the minimum requirements will raise an exception.